Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by fundamentally avoiding data movement and reducing data access latency & energy. Many recent studies show that memory-centric computing can greatly improve system performance and energy efficiency. Major industrial vendors and startup companies have also recently introduced memory chips that have sophisticated computation capabilities. This talk describes promising ongoing research and development efforts in memory-centric computing. We classify such efforts into two major fundamental categories: 1) processing using memory, which exploits analog operational properties of memory structures to perform massively-parallel operations in memory, and 2) processing near memory, which integrates processing capability in memory controllers, the logic layer of 3D-stacked memory technologies, or memory chips to enable high-bandwidth and low-latency memory access to near-memory logic. We show both types of architectures (and their combination) can enable orders of magnitude improvements in performance and energy consumption of many important workloads, such as graph analytics, databases, machine learning, video processing, climate modeling, genome analysis. We discuss adoption challenges for the memory-centric computing paradigm and conclude with some research & development opportunities.

翻译：以存储为中心的计算旨在在数据生成和存储的所有位置或其附近实现计算能力。通过从根本上避免数据移动并降低数据访问延迟与能耗，该方法能够大幅减少数据访问与移动带来的负面性能和能耗影响。近期众多研究表明，以存储为中心的计算可显著提升系统性能与能效。主要工业厂商及初创公司也已推出具备复杂计算能力的存储芯片。本报告阐述了以存储为中心计算领域前景广阔的在研与开发工作。我们将此类工作分为两大基本类别：1）利用存储进行计算——利用存储结构的模拟操作特性在存储内执行大规模并行运算；2）近存储计算——在存储控制器、3D堆叠存储技术逻辑层或存储芯片中集成处理能力，使近存储逻辑能够实现高带宽、低延迟的存储访问。我们证明，这两类架构（及其组合）能够使图分析、数据库、机器学习、视频处理、气候建模、基因组分析等重要负载的性能与能耗提升数个数量级。我们探讨了以存储为中心计算范式的应用挑战，并总结了若干研发机遇。